Overview

Dataset statistics

Number of variables21
Number of observations10000
Missing cells1280
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory168.0 B

Variable types

Text1
Numeric12
Categorical7
DateTime1

Alerts

debt_to_income is highly overall correlated with income and 1 other fieldsHigh correlation
income is highly overall correlated with debt_to_income and 1 other fieldsHigh correlation
loan_amount is highly overall correlated with debt_to_incomeHigh correlation
target_default_risk is highly overall correlated with incomeHigh correlation
recent_default is highly imbalanced (72.6%)Imbalance
income has 318 (3.2%) missing valuesMissing
savings has 311 (3.1%) missing valuesMissing
monthly_expenses has 325 (3.2%) missing valuesMissing
credit_score has 326 (3.3%) missing valuesMissing
customer_id has unique valuesUnique
num_dependents has 2984 (29.8%) zerosZeros
signup_dayofweek has 1454 (14.5%) zerosZeros

Reproduction

Analysis started2025-12-13 14:39:34.827684
Analysis finished2025-12-13 14:40:02.382745
Duration27.56 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

customer_id
Text

Unique 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2025-12-13T20:10:03.165863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowCUST006253
2nd rowCUST004685
3rd rowCUST001732
4th rowCUST004743
5th rowCUST004522
ValueCountFrequency (%)
cust0050521
 
< 0.1%
cust0053121
 
< 0.1%
cust0024341
 
< 0.1%
cust0069501
 
< 0.1%
cust0007701
 
< 0.1%
cust0016861
 
< 0.1%
cust0083231
 
< 0.1%
cust0055791
 
< 0.1%
cust0044271
 
< 0.1%
cust0004671
 
< 0.1%
Other values (9990)9990
99.9%
2025-12-13T20:10:04.096271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
023999
24.0%
C10000
10.0%
U10000
10.0%
S10000
10.0%
T10000
10.0%
14001
 
4.0%
64000
 
4.0%
44000
 
4.0%
24000
 
4.0%
84000
 
4.0%
Other values (4)16000
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
023999
24.0%
C10000
10.0%
U10000
10.0%
S10000
10.0%
T10000
10.0%
14001
 
4.0%
64000
 
4.0%
44000
 
4.0%
24000
 
4.0%
84000
 
4.0%
Other values (4)16000
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
023999
24.0%
C10000
10.0%
U10000
10.0%
S10000
10.0%
T10000
10.0%
14001
 
4.0%
64000
 
4.0%
44000
 
4.0%
24000
 
4.0%
84000
 
4.0%
Other values (4)16000
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)100000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
023999
24.0%
C10000
10.0%
U10000
10.0%
S10000
10.0%
T10000
10.0%
14001
 
4.0%
64000
 
4.0%
44000
 
4.0%
24000
 
4.0%
84000
 
4.0%
Other values (4)16000
16.0%

age
Real number (ℝ)

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.8616
Minimum18
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-13T20:10:04.289725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q132
median46
Q360
95-th percentile72
Maximum74
Range56
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.457987
Coefficient of variation (CV)0.35886203
Kurtosis-1.1934533
Mean45.8616
Median Absolute Deviation (MAD)14
Skewness0.017032931
Sum458616
Variance270.86533
MonotonicityNot monotonic
2025-12-13T20:10:04.492113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54209
 
2.1%
73209
 
2.1%
53199
 
2.0%
36194
 
1.9%
34192
 
1.9%
48191
 
1.9%
46190
 
1.9%
25189
 
1.9%
21187
 
1.9%
58186
 
1.9%
Other values (47)8054
80.5%
ValueCountFrequency (%)
18165
1.7%
19177
1.8%
20186
1.9%
21187
1.9%
22177
1.8%
23166
1.7%
24174
1.7%
25189
1.9%
26185
1.8%
27168
1.7%
ValueCountFrequency (%)
74181
1.8%
73209
2.1%
72165
1.7%
71172
1.7%
70179
1.8%
69166
1.7%
68163
1.6%
67159
1.6%
66167
1.7%
65148
1.5%

income
Real number (ℝ)

High correlation  Missing 

Distinct9107
Distinct (%)94.1%
Missing318
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean59712.871
Minimum20001
Maximum402769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-13T20:10:04.692423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20001
5-th percentile22169.1
Q131300.5
median47301.5
Q375164.25
95-th percentile140669.3
Maximum402769
Range382768
Interquartile range (IQR)43863.75

Descriptive statistics

Standard deviation39865.231
Coefficient of variation (CV)0.66761538
Kurtosis5.8091939
Mean59712.871
Median Absolute Deviation (MAD)19159
Skewness1.9817283
Sum5.7814002 × 108
Variance1.5892367 × 109
MonotonicityNot monotonic
2025-12-13T20:10:04.901949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
212324
 
< 0.1%
207024
 
< 0.1%
210783
 
< 0.1%
339733
 
< 0.1%
741783
 
< 0.1%
278523
 
< 0.1%
223163
 
< 0.1%
310083
 
< 0.1%
292243
 
< 0.1%
365763
 
< 0.1%
Other values (9097)9650
96.5%
(Missing)318
 
3.2%
ValueCountFrequency (%)
200011
< 0.1%
200031
< 0.1%
200121
< 0.1%
200181
< 0.1%
200241
< 0.1%
200271
< 0.1%
200312
< 0.1%
200341
< 0.1%
200401
< 0.1%
200461
< 0.1%
ValueCountFrequency (%)
4027691
< 0.1%
3821061
< 0.1%
3815921
< 0.1%
3700731
< 0.1%
3238121
< 0.1%
3079411
< 0.1%
3028521
< 0.1%
2935041
< 0.1%
2897611
< 0.1%
2895701
< 0.1%

savings
Real number (ℝ)

Missing 

Distinct6498
Distinct (%)67.1%
Missing311
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean5039.9225
Minimum0
Maximum44644
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-13T20:10:05.115657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile263.4
Q11476
median3499
Q36986
95-th percentile15009
Maximum44644
Range44644
Interquartile range (IQR)5510

Descriptive statistics

Standard deviation5041.7936
Coefficient of variation (CV)1.0003713
Kurtosis5.8899705
Mean5039.9225
Median Absolute Deviation (MAD)2415
Skewness2.0173309
Sum48831809
Variance25419683
MonotonicityNot monotonic
2025-12-13T20:10:05.353472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13088
 
0.1%
2757
 
0.1%
466
 
0.1%
9566
 
0.1%
446
 
0.1%
2746
 
0.1%
18036
 
0.1%
7236
 
0.1%
3626
 
0.1%
32526
 
0.1%
Other values (6488)9626
96.3%
(Missing)311
 
3.1%
ValueCountFrequency (%)
02
< 0.1%
13
< 0.1%
21
 
< 0.1%
32
< 0.1%
43
< 0.1%
54
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
82
< 0.1%
102
< 0.1%
ValueCountFrequency (%)
446441
< 0.1%
429081
< 0.1%
422721
< 0.1%
406881
< 0.1%
403191
< 0.1%
378591
< 0.1%
368421
< 0.1%
363461
< 0.1%
355801
< 0.1%
355131
< 0.1%

monthly_expenses
Real number (ℝ)

Missing 

Distinct3068
Distinct (%)31.7%
Missing325
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean2082.2096
Minimum200
Maximum28664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-13T20:10:05.561287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile700.7
Q11471
median2007
Q32557
95-th percentile3355
Maximum28664
Range28464
Interquartile range (IQR)1086

Descriptive statistics

Standard deviation1385.9918
Coefficient of variation (CV)0.66563509
Kurtosis130.18133
Mean2082.2096
Median Absolute Deviation (MAD)543
Skewness9.1372786
Sum20145378
Variance1920973.2
MonotonicityNot monotonic
2025-12-13T20:10:05.773480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200124
 
1.2%
149514
 
0.1%
206413
 
0.1%
152813
 
0.1%
236112
 
0.1%
160012
 
0.1%
256811
 
0.1%
186511
 
0.1%
220811
 
0.1%
241011
 
0.1%
Other values (3058)9443
94.4%
(Missing)325
 
3.2%
ValueCountFrequency (%)
200124
1.2%
2021
 
< 0.1%
2041
 
< 0.1%
2091
 
< 0.1%
2221
 
< 0.1%
2231
 
< 0.1%
2251
 
< 0.1%
2271
 
< 0.1%
2302
 
< 0.1%
2322
 
< 0.1%
ValueCountFrequency (%)
286641
< 0.1%
270161
< 0.1%
258321
< 0.1%
258081
< 0.1%
257121
< 0.1%
253441
< 0.1%
244801
< 0.1%
234001
< 0.1%
229841
< 0.1%
219441
< 0.1%

num_dependents
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2142
Minimum0
Maximum7
Zeros2984
Zeros (%)29.8%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-13T20:10:05.932154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1089821
Coefficient of variation (CV)0.91334386
Kurtosis0.98351171
Mean1.2142
Median Absolute Deviation (MAD)1
Skewness0.94154018
Sum12142
Variance1.2298413
MonotonicityNot monotonic
2025-12-13T20:10:06.076866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
13603
36.0%
02984
29.8%
22166
21.7%
3891
 
8.9%
4275
 
2.8%
557
 
0.6%
619
 
0.2%
75
 
0.1%
ValueCountFrequency (%)
02984
29.8%
13603
36.0%
22166
21.7%
3891
 
8.9%
4275
 
2.8%
557
 
0.6%
619
 
0.2%
75
 
0.1%
ValueCountFrequency (%)
75
 
0.1%
619
 
0.2%
557
 
0.6%
4275
 
2.8%
3891
 
8.9%
22166
21.7%
13603
36.0%
02984
29.8%

credit_score
Real number (ℝ)

Missing 

Distinct9647
Distinct (%)99.7%
Missing326
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean650.15544
Minimum363.07712
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-13T20:10:06.262099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum363.07712
5-th percentile537.24136
Q1602.18989
median649.80832
Q3697.53743
95-th percentile765.93973
Maximum850
Range486.92288
Interquartile range (IQR)95.347538

Descriptive statistics

Standard deviation69.918297
Coefficient of variation (CV)0.10754089
Kurtosis-0.0916615
Mean650.15544
Median Absolute Deviation (MAD)47.685985
Skewness0.011038873
Sum6289603.7
Variance4888.5682
MonotonicityNot monotonic
2025-12-13T20:10:06.470045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85028
 
0.3%
630.76576361
 
< 0.1%
699.81752861
 
< 0.1%
784.58822041
 
< 0.1%
642.8127851
 
< 0.1%
711.7010251
 
< 0.1%
631.21639971
 
< 0.1%
682.63706311
 
< 0.1%
590.86275851
 
< 0.1%
612.91831961
 
< 0.1%
Other values (9637)9637
96.4%
(Missing)326
 
3.3%
ValueCountFrequency (%)
363.07711611
< 0.1%
380.35385331
< 0.1%
411.50782071
< 0.1%
418.98340021
< 0.1%
424.38759991
< 0.1%
425.91404491
< 0.1%
426.2726231
< 0.1%
427.02263021
< 0.1%
428.0665981
< 0.1%
429.78593211
< 0.1%
ValueCountFrequency (%)
85028
0.3%
849.09422471
 
< 0.1%
848.95323331
 
< 0.1%
848.89564861
 
< 0.1%
848.48543941
 
< 0.1%
847.86104441
 
< 0.1%
844.85167381
 
< 0.1%
844.54167711
 
< 0.1%
841.96920531
 
< 0.1%
840.40555921
 
< 0.1%

loan_amount
Real number (ℝ)

High correlation 

Distinct7999
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16214.797
Minimum1000
Maximum441190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-13T20:10:06.682518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q18508.5
median15174.5
Q321843.75
95-th percentile31818.15
Maximum441190
Range440190
Interquartile range (IQR)13335.25

Descriptive statistics

Standard deviation16081.647
Coefficient of variation (CV)0.99178836
Kurtosis209.5393
Mean16214.797
Median Absolute Deviation (MAD)6669.5
Skewness11.216031
Sum1.6214797 × 108
Variance2.5861936 × 108
MonotonicityNot monotonic
2025-12-13T20:10:06.896036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000740
 
7.4%
129287
 
0.1%
100006
 
0.1%
212854
 
< 0.1%
200014
 
< 0.1%
146114
 
< 0.1%
90154
 
< 0.1%
187414
 
< 0.1%
196734
 
< 0.1%
225764
 
< 0.1%
Other values (7989)9219
92.2%
ValueCountFrequency (%)
1000740
7.4%
10041
 
< 0.1%
10051
 
< 0.1%
10131
 
< 0.1%
10211
 
< 0.1%
10241
 
< 0.1%
10291
 
< 0.1%
10351
 
< 0.1%
10491
 
< 0.1%
10601
 
< 0.1%
ValueCountFrequency (%)
4411901
< 0.1%
4047501
< 0.1%
4001201
< 0.1%
3381201
< 0.1%
3159201
< 0.1%
2913501
< 0.1%
2836901
< 0.1%
2767701
< 0.1%
2632901
< 0.1%
2547101
< 0.1%

loan_term_months
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.642
Minimum12
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-13T20:10:07.054455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12
Q136
median48
Q360
95-th percentile72
Maximum72
Range60
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.475134
Coefficient of variation (CV)0.33905469
Kurtosis-0.44393278
Mean45.642
Median Absolute Deviation (MAD)12
Skewness-0.19759142
Sum456420
Variance239.47978
MonotonicityNot monotonic
2025-12-13T20:10:07.182667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
482998
30.0%
362523
25.2%
602008
20.1%
721003
 
10.0%
24948
 
9.5%
12520
 
5.2%
ValueCountFrequency (%)
12520
 
5.2%
24948
 
9.5%
362523
25.2%
482998
30.0%
602008
20.1%
721003
 
10.0%
ValueCountFrequency (%)
721003
 
10.0%
602008
20.1%
482998
30.0%
362523
25.2%
24948
 
9.5%
12520
 
5.2%

employment_years
Real number (ℝ)

Distinct182
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.39701
Minimum0
Maximum21.5
Zeros44
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-13T20:10:07.369463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q12.7
median5.1
Q37.7
95-th percentile11.5
Maximum21.5
Range21.5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4136997
Coefficient of variation (CV)0.63251683
Kurtosis-0.14701843
Mean5.39701
Median Absolute Deviation (MAD)2.5
Skewness0.52890029
Sum53970.1
Variance11.653345
MonotonicityNot monotonic
2025-12-13T20:10:07.575122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.4124
 
1.2%
2.9121
 
1.2%
3.8119
 
1.2%
5.3119
 
1.2%
0.1118
 
1.2%
3.6118
 
1.2%
4.9116
 
1.2%
4.7116
 
1.2%
4.6114
 
1.1%
2.5114
 
1.1%
Other values (172)8821
88.2%
ValueCountFrequency (%)
044
 
0.4%
0.1118
1.2%
0.2101
1.0%
0.391
0.9%
0.478
0.8%
0.592
0.9%
0.683
0.8%
0.797
1.0%
0.889
0.9%
0.999
1.0%
ValueCountFrequency (%)
21.51
< 0.1%
19.51
< 0.1%
18.91
< 0.1%
18.41
< 0.1%
18.22
< 0.1%
17.91
< 0.1%
17.81
< 0.1%
17.71
< 0.1%
17.62
< 0.1%
17.51
< 0.1%

home_ownership
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
RENT
4524 
OWN
2526 
MORTGAGE
2498 
OTHER
 
452

Length

Max length8
Median length5
Mean length4.7918
Min length3

Characters and Unicode

Total characters47918
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowOWN
4th rowOWN
5th rowMORTGAGE

Common Values

ValueCountFrequency (%)
RENT4524
45.2%
OWN2526
25.3%
MORTGAGE2498
25.0%
OTHER452
 
4.5%

Length

2025-12-13T20:10:07.765116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T20:10:07.891932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
rent4524
45.2%
own2526
25.3%
mortgage2498
25.0%
other452
 
4.5%

Most occurring characters

ValueCountFrequency (%)
R7474
15.6%
E7474
15.6%
T7474
15.6%
N7050
14.7%
O5476
11.4%
G4996
10.4%
W2526
 
5.3%
M2498
 
5.2%
A2498
 
5.2%
H452
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)47918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R7474
15.6%
E7474
15.6%
T7474
15.6%
N7050
14.7%
O5476
11.4%
G4996
10.4%
W2526
 
5.3%
M2498
 
5.2%
A2498
 
5.2%
H452
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)47918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R7474
15.6%
E7474
15.6%
T7474
15.6%
N7050
14.7%
O5476
11.4%
G4996
10.4%
W2526
 
5.3%
M2498
 
5.2%
A2498
 
5.2%
H452
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)47918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R7474
15.6%
E7474
15.6%
T7474
15.6%
N7050
14.7%
O5476
11.4%
G4996
10.4%
W2526
 
5.3%
M2498
 
5.2%
A2498
 
5.2%
H452
 
0.9%

education
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Bachelors
4443 
HS
2546 
Masters
1962 
Other
500 
PhD
462 

Length

Max length9
Median length8
Mean length6.3395
Min length2

Characters and Unicode

Total characters63395
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHS
2nd rowBachelors
3rd rowBachelors
4th rowHS
5th rowMasters

Common Values

ValueCountFrequency (%)
Bachelors4443
44.4%
HS2546
25.5%
Masters1962
19.6%
Other500
 
5.0%
PhD462
 
4.6%
Bachlors87
 
0.9%

Length

2025-12-13T20:10:08.078489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T20:10:08.211808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
bachelors4443
44.4%
hs2546
25.5%
masters1962
19.6%
other500
 
5.0%
phd462
 
4.6%
bachlors87
 
0.9%

Most occurring characters

ValueCountFrequency (%)
s8454
13.3%
r6992
11.0%
e6905
10.9%
a6492
10.2%
h5492
8.7%
c4530
7.1%
l4530
7.1%
B4530
7.1%
o4530
7.1%
H2546
 
4.0%
Other values (6)8394
13.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)63395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s8454
13.3%
r6992
11.0%
e6905
10.9%
a6492
10.2%
h5492
8.7%
c4530
7.1%
l4530
7.1%
B4530
7.1%
o4530
7.1%
H2546
 
4.0%
Other values (6)8394
13.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)63395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s8454
13.3%
r6992
11.0%
e6905
10.9%
a6492
10.2%
h5492
8.7%
c4530
7.1%
l4530
7.1%
B4530
7.1%
o4530
7.1%
H2546
 
4.0%
Other values (6)8394
13.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)63395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s8454
13.3%
r6992
11.0%
e6905
10.9%
a6492
10.2%
h5492
8.7%
c4530
7.1%
l4530
7.1%
B4530
7.1%
o4530
7.1%
H2546
 
4.0%
Other values (6)8394
13.2%

marital_status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Single
4486 
Married
4002 
Divorced
1000 
Widowed
512 

Length

Max length8
Median length7
Mean length6.6514
Min length6

Characters and Unicode

Total characters66514
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowSingle

Common Values

ValueCountFrequency (%)
Single4486
44.9%
Married4002
40.0%
Divorced1000
 
10.0%
Widowed512
 
5.1%

Length

2025-12-13T20:10:08.395633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T20:10:08.524562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
single4486
44.9%
married4002
40.0%
divorced1000
 
10.0%
widowed512
 
5.1%

Most occurring characters

ValueCountFrequency (%)
i10000
15.0%
e10000
15.0%
r9004
13.5%
d6026
9.1%
g4486
6.7%
l4486
6.7%
n4486
6.7%
S4486
6.7%
a4002
6.0%
M4002
6.0%
Other values (6)5536
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)66514
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i10000
15.0%
e10000
15.0%
r9004
13.5%
d6026
9.1%
g4486
6.7%
l4486
6.7%
n4486
6.7%
S4486
6.7%
a4002
6.0%
M4002
6.0%
Other values (6)5536
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)66514
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i10000
15.0%
e10000
15.0%
r9004
13.5%
d6026
9.1%
g4486
6.7%
l4486
6.7%
n4486
6.7%
S4486
6.7%
a4002
6.0%
M4002
6.0%
Other values (6)5536
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)66514
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i10000
15.0%
e10000
15.0%
r9004
13.5%
d6026
9.1%
g4486
6.7%
l4486
6.7%
n4486
6.7%
S4486
6.7%
a4002
6.0%
M4002
6.0%
Other values (6)5536
8.3%

region
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
East
2553 
South
2523 
North
2479 
West
2445 

Length

Max length5
Median length5
Mean length4.5002
Min length4

Characters and Unicode

Total characters45002
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWest
2nd rowEast
3rd rowEast
4th rowSouth
5th rowWest

Common Values

ValueCountFrequency (%)
East2553
25.5%
South2523
25.2%
North2479
24.8%
West2445
24.4%

Length

2025-12-13T20:10:08.679374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T20:10:08.794190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
east2553
25.5%
south2523
25.2%
north2479
24.8%
west2445
24.4%

Most occurring characters

ValueCountFrequency (%)
t10000
22.2%
o5002
11.1%
h5002
11.1%
s4998
11.1%
E2553
 
5.7%
a2553
 
5.7%
S2523
 
5.6%
u2523
 
5.6%
N2479
 
5.5%
r2479
 
5.5%
Other values (2)4890
10.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)45002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t10000
22.2%
o5002
11.1%
h5002
11.1%
s4998
11.1%
E2553
 
5.7%
a2553
 
5.7%
S2523
 
5.6%
u2523
 
5.6%
N2479
 
5.5%
r2479
 
5.5%
Other values (2)4890
10.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t10000
22.2%
o5002
11.1%
h5002
11.1%
s4998
11.1%
E2553
 
5.7%
a2553
 
5.7%
S2523
 
5.6%
u2523
 
5.6%
N2479
 
5.5%
r2479
 
5.5%
Other values (2)4890
10.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t10000
22.2%
o5002
11.1%
h5002
11.1%
s4998
11.1%
E2553
 
5.7%
a2553
 
5.7%
S2523
 
5.6%
u2523
 
5.6%
N2479
 
5.5%
r2479
 
5.5%
Other values (2)4890
10.9%

recent_default
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
0
9530 
1
 
470

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09530
95.3%
1470
 
4.7%

Length

2025-12-13T20:10:08.955094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T20:10:09.077373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
09530
95.3%
1470
 
4.7%

Most occurring characters

ValueCountFrequency (%)
09530
95.3%
1470
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
09530
95.3%
1470
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
09530
95.3%
1470
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
09530
95.3%
1470
 
4.7%

has_credit_card
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
6948 
0
3052 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
16948
69.5%
03052
30.5%

Length

2025-12-13T20:10:09.216904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T20:10:09.327286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
16948
69.5%
03052
30.5%

Most occurring characters

ValueCountFrequency (%)
16948
69.5%
03052
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16948
69.5%
03052
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16948
69.5%
03052
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16948
69.5%
03052
30.5%
Distinct1982
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Minimum2018-01-01 00:00:00
Maximum2023-06-23 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-13T20:10:09.471492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:10:09.695790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

signup_dayofweek
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0119
Minimum0
Maximum6
Zeros1454
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-13T20:10:09.866472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.003986
Coefficient of variation (CV)0.6653561
Kurtosis-1.2553989
Mean3.0119
Median Absolute Deviation (MAD)2
Skewness-0.017084941
Sum30119
Variance4.01596
MonotonicityNot monotonic
2025-12-13T20:10:10.009125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
51488
14.9%
21461
14.6%
01454
14.5%
41430
14.3%
61420
14.2%
31385
13.9%
11362
13.6%
ValueCountFrequency (%)
01454
14.5%
11362
13.6%
21461
14.6%
31385
13.9%
41430
14.3%
51488
14.9%
61420
14.2%
ValueCountFrequency (%)
61420
14.2%
51488
14.9%
41430
14.3%
31385
13.9%
21461
14.6%
11362
13.6%
01454
14.5%

debt_to_income
Real number (ℝ)

High correlation 

Distinct1261
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3581564
Minimum0.004
Maximum2.031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2025-12-13T20:10:10.381023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile0.024
Q10.132
median0.275
Q30.508
95-th percentile0.96
Maximum2.031
Range2.027
Interquartile range (IQR)0.376

Descriptive statistics

Standard deviation0.30260645
Coefficient of variation (CV)0.84490031
Kurtosis2.0629225
Mean0.3581564
Median Absolute Deviation (MAD)0.17
Skewness1.3541286
Sum3581.564
Variance0.091570665
MonotonicityNot monotonic
2025-12-13T20:10:10.592223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02440
 
0.4%
0.01740
 
0.4%
0.02837
 
0.4%
0.01136
 
0.4%
0.01234
 
0.3%
0.02633
 
0.3%
0.01832
 
0.3%
0.01531
 
0.3%
0.0231
 
0.3%
0.1331
 
0.3%
Other values (1251)9655
96.5%
ValueCountFrequency (%)
0.0045
 
0.1%
0.0054
 
< 0.1%
0.00616
0.2%
0.00722
0.2%
0.00823
0.2%
0.00922
0.2%
0.0123
0.2%
0.01136
0.4%
0.01234
0.3%
0.01327
0.3%
ValueCountFrequency (%)
2.0311
< 0.1%
1.9381
< 0.1%
1.9192
< 0.1%
1.9161
< 0.1%
1.8661
< 0.1%
1.8211
< 0.1%
1.821
< 0.1%
1.8021
< 0.1%
1.7821
< 0.1%
1.771
< 0.1%

sin_age
Real number (ℝ)

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.10038733
Minimum-0.99992326
Maximum0.97384763
Zeros0
Zeros (%)0.0%
Negative5473
Negative (%)54.7%
Memory size78.3 KiB
2025-12-13T20:10:10.816206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.99992326
5-th percentile-0.993691
Q1-0.7568025
median-0.15774569
Q30.51550137
95-th percentile0.90929743
Maximum0.97384763
Range1.9737709
Interquartile range (IQR)1.2723039

Descriptive statistics

Standard deviation0.66742807
Coefficient of variation (CV)-6.6485288
Kurtosis-1.4372524
Mean-0.10038733
Median Absolute Deviation (MAD)0.65185905
Skewness0.13670034
Sum-1003.8733
Variance0.44546023
MonotonicityNot monotonic
2025-12-13T20:10:11.046186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7727644876209
 
2.1%
0.8504366206209
 
2.1%
-0.8322674422199
 
2.0%
-0.4425204433194
 
1.9%
-0.255541102192
 
1.9%
-0.9961646088191
 
1.9%
-0.9936910036190
 
1.9%
0.5984721441189
 
1.9%
0.8632093666187
 
1.9%
-0.4646021794186
 
1.9%
Other values (47)8054
80.5%
ValueCountFrequency (%)
-0.9999232576180
1.8%
-0.9961646088191
1.9%
-0.9936910036190
1.9%
-0.9824526126185
1.8%
-0.9775301177170
1.7%
-0.9589242747177
1.8%
-0.9516020739147
1.5%
-0.9258146823154
1.5%
-0.9161659367164
1.6%
-0.8834546557164
1.6%
ValueCountFrequency (%)
0.9738476309165
1.7%
0.9463000877177
1.8%
0.9092974268186
1.9%
0.8987080958181
1.8%
0.8632093666187
1.9%
0.8504366206209
2.1%
0.8084964038177
1.8%
0.7936678638165
1.7%
0.7457052122166
1.7%
0.7289690401172
1.7%

target_default_risk
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
1
5132 
0
4868 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
15132
51.3%
04868
48.7%

Length

2025-12-13T20:10:11.252737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-13T20:10:11.370654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15132
51.3%
04868
48.7%

Most occurring characters

ValueCountFrequency (%)
15132
51.3%
04868
48.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15132
51.3%
04868
48.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15132
51.3%
04868
48.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15132
51.3%
04868
48.7%

Interactions

2025-12-13T20:09:59.316680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:37.019735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:38.933336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:41.090268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:43.126035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:45.167202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:47.213109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:49.121025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:51.250807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:53.318200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:55.217488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:57.174889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:59.482188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:37.170903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:39.110569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:41.232589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:43.290274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:45.319885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:47.365038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:49.269570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:51.405026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:53.463011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:55.371313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:57.341244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:59.680962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:37.328260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:39.296747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:41.397300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:43.476012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:45.489980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:47.531390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:49.447171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:51.593225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:53.633790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:55.550519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:57.532683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:59.849088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:37.472829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:39.471427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:41.546249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:43.632779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:45.647301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:47.682979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:49.605214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:51.744851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:53.797476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:55.703001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:57.705596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:10:00.050277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:37.639791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:39.664300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:41.728384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:43.796126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:45.819366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:47.842098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:49.766807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:51.934071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:53.954397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:55.881295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:57.898001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:10:00.237325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:37.791961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:39.838249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:41.901033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:43.961129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:45.993642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:47.994348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:49.929456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:52.101577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:54.125915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:56.043628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:58.070283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:10:00.406599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:37.935328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:40.003160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:42.070195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:44.134452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:46.168861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:48.145159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:50.083861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:52.272655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:54.273655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:56.198354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:58.230368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:10:00.575831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:38.097773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:40.172297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:42.233983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:44.295424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:46.319917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:48.304418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:50.238689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:52.433142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:54.421052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:56.356227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:58.401942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:10:00.758904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:38.255683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:40.349227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:42.400244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:44.465029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:46.490867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:48.455259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:50.416907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:52.613268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:54.581242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:56.513108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:58.581551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:10:00.932597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:38.412328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:40.513175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:42.562636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:44.634936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:46.646137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:48.601410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:50.738365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:52.781524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:54.735566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:56.659325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:58.744824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:10:01.128942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:38.586143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:40.702003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:42.749099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:44.794941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:46.811947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:48.757422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:50.896659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:52.938831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:54.887280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:56.824422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:58.930822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:10:01.316898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:38.763416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:40.898463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:42.947566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:44.968841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:46.993072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:48.954190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:51.073148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:53.139872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:55.057673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:57.004779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-13T20:09:59.129992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-13T20:10:11.491604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
agecredit_scoredebt_to_incomeeducationemployment_yearshas_credit_cardhome_ownershipincomeloan_amountloan_term_monthsmarital_statusmonthly_expensesnum_dependentsrecent_defaultregionsavingssignup_dayofweeksin_agetarget_default_risk
age1.000-0.0000.0220.0150.0050.0190.000-0.0270.012-0.0040.000-0.0070.0060.0250.0000.0080.005-0.1360.024
credit_score-0.0001.000-0.0050.015-0.0180.0000.0000.0040.0020.0050.000-0.0000.0040.0000.0180.0110.009-0.0050.087
debt_to_income0.022-0.0051.0000.000-0.0150.0170.000-0.5870.751-0.0170.010-0.0010.0010.0000.0070.004-0.017-0.0140.485
education0.0150.0150.0001.0000.0150.0000.0000.0030.0120.0100.0000.0190.0080.0000.0000.0000.0100.0000.000
employment_years0.005-0.018-0.0150.0151.0000.0160.0000.006-0.0090.0130.0000.004-0.0040.0000.028-0.0020.012-0.0290.025
has_credit_card0.0190.0000.0170.0000.0161.0000.0000.0190.0120.0000.0000.0320.0140.0000.0200.0000.0260.0060.058
home_ownership0.0000.0000.0000.0000.0000.0001.0000.0000.0160.0070.0060.0040.0000.0000.0000.0070.0050.0200.018
income-0.0270.004-0.5870.0030.0060.0190.0001.0000.0050.0090.000-0.0040.0060.0340.0000.0030.0060.0100.735
loan_amount0.0120.0020.7510.012-0.0090.0120.0160.0051.000-0.0090.0000.0090.0070.0140.0000.004-0.020-0.0110.025
loan_term_months-0.0040.005-0.0170.0100.0130.0000.0070.009-0.0091.0000.027-0.009-0.0010.0200.011-0.002-0.0050.0020.021
marital_status0.0000.0000.0100.0000.0000.0000.0060.0000.0000.0271.0000.0130.0000.0000.0140.0090.0000.0000.000
monthly_expenses-0.007-0.000-0.0010.0190.0040.0320.004-0.0040.009-0.0090.0131.0000.0120.0000.000-0.0130.0030.0090.000
num_dependents0.0060.0040.0010.008-0.0040.0140.0000.0060.007-0.0010.0000.0121.0000.0170.016-0.004-0.002-0.0070.000
recent_default0.0250.0000.0000.0000.0000.0000.0000.0340.0140.0200.0000.0000.0171.0000.0000.0000.0000.0130.000
region0.0000.0180.0070.0000.0280.0200.0000.0000.0000.0110.0140.0000.0160.0001.0000.0000.0000.0000.004
savings0.0080.0110.0040.000-0.0020.0000.0070.0030.004-0.0020.009-0.013-0.0040.0000.0001.000-0.009-0.0070.046
signup_dayofweek0.0050.009-0.0170.0100.0120.0260.0050.006-0.020-0.0050.0000.003-0.0020.0000.000-0.0091.000-0.0040.000
sin_age-0.136-0.005-0.0140.000-0.0290.0060.0200.010-0.0110.0020.0000.009-0.0070.0130.000-0.007-0.0041.0000.000
target_default_risk0.0240.0870.4850.0000.0250.0580.0180.7350.0250.0210.0000.0000.0000.0000.0040.0460.0000.0001.000

Missing values

2025-12-13T20:10:01.611053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-13T20:10:01.955225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-13T20:10:02.254654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

customer_idageincomesavingsmonthly_expensesnum_dependentscredit_scoreloan_amountloan_term_monthsemployment_yearshome_ownershipeducationmarital_statusregionrecent_defaulthas_credit_cardsignup_datesignup_dayofweekdebt_to_incomesin_agetarget_default_risk
0CUST0062533066737.011155.02272.02605.07620426965.0483.9RENTHSSingleWest112020-07-0560.4040.1411201
1CUST0046852270740.0997.01934.01683.2919674681.0360.7RENTBachelorsMarriedEast002018-10-0320.0660.8084961
2CUST0017326838890.01929.01696.00658.00336012633.0722.2OWNBachelorsSingleEast012018-05-3020.3250.4941130
3CUST0047434929049.06284.02485.01707.47786420881.0362.7OWNHSMarriedSouth012018-04-2260.719-0.9824530
4CUST0045227460063.0924.03179.02564.76851119438.03610.3MORTGAGEMastersSingleWest002019-12-0310.3240.8987081
5CUST0063415637852.04826.03055.03686.86352915328.0481.3RENTMastersSingleSouth012018-11-0830.405-0.6312670
6CUST0005771964635.05240.02737.02564.79994223469.0486.7RENTHSDivorcedNorth012021-12-1410.3630.9463001
7CUST0052034458003.06113.01607.01590.3098541000.0480.5OWNOtherSingleEast002019-10-0220.017-0.9516021
8CUST0063641841132.014936.01414.02616.70103022800.0722.1OWNPhDMarriedEast012018-04-1820.5540.9738480
9CUST0004402951038.012639.02514.01604.9028076050.0606.0RENTMastersSingleEast002022-02-0550.1190.2392490
customer_idageincomesavingsmonthly_expensesnum_dependentscredit_scoreloan_amountloan_term_monthsemployment_yearshome_ownershipeducationmarital_statusregionrecent_defaulthas_credit_cardsignup_datesignup_dayofweekdebt_to_incomesin_agetarget_default_risk
9990CUST0083235932742.01186.01812.01631.21640034003.0485.0RENTHSSingleWest002023-01-0751.038-0.3738770
9991CUST0055792546494.09050.02738.00682.63706320116.0485.1RENTMastersMarriedWest012018-02-2450.4330.5984721
9992CUST00442746100954.0NaN1049.02590.86275811164.07211.7MORTGAGEBachelorsSingleSouth012020-02-2620.111-0.9936911
9993CUST0004673631124.02188.01427.01612.91832032198.0360.3RENTBachelorsSingleEast012023-04-2361.034-0.4425200
9994CUST0062664261252.01911.01042.01774.95348513243.0480.9MORTGAGEHSMarriedEast002018-06-1430.216-0.8715761
9995CUST0057355444507.05975.02520.01699.63335231089.0485.3RENTHSSingleEast012020-02-2730.699-0.7727641
9996CUST0051925020651.010203.01020.03680.7740668977.0609.6RENTPhDDivorcedNorth002018-08-2330.435-0.9589240
9997CUST0053914333827.03848.02562.01655.56274824319.0604.3OTHERHSMarriedWest002019-01-1840.719-0.9161660
9998CUST0008614438273.018880.01060.02653.2776451000.02411.4MORTGAGEOtherSingleNorth012019-08-0460.026-0.9516020
9999CUST0072713053614.06201.01310.01663.9755565205.0609.8RENTBachelorsDivorcedNorth012018-03-0350.0970.1411201